Financial Cat Scans

Cost, Price, and The Space Between

I sing my heart out to the wide open spaces
Pet Townshend

This month, we’ll study the difference between cost and price, why it matters, and how knowing how both behave in tandem is the key to success.

I know from our analytics that most of you are in the business of working out costs and prices. For many, it is hard to separate the two, especially if you work in or with the government. Today’s analysis directs itself to commercial operations. In a future newsletter, we’ll look at how to adapt this framework to the public sector.

All too often in business, someone comes up with a seemingly great idea and gets fellow workers excited about it. It gets pushed into production. Producers then wait to see what the market will bear for it, often falling short of projections.

What if you could change the paradigm?

Suppose you could see market openings and limits and test sample specifications and sales targets before you commit resources to a configuration. That would improve your chances of success.

You’ll have to work to enable this vision, but you will find it worthwhile.

When we at Hypernomics look at a market, we begin with Demand. As shown below as the red plane, that means finding the ordered pairs for Quantity and Price. We create a series of price bins (either equally spaced or binned by geometric or Fibonacci methods) and determine the ordered pairs (as the purple hexagons) representing each bin’s average price and total Quantity. Then we run a regression curve through them, which represents Aggregate Market Demand.

To the left of that curve, we find the Demand Frontier, a regression through the outermost points on the Demand Plane. This curve shows the limit of the products this market can absorb over time. As markets mature, the Aggregate Market Demand and Demand Frontier slopes often approximate one another.

If we examine the points closely, we’ll notice a price gap. Using its midpoint, we would find the 1) Quantity limit the market will support at that price (the vertical red line coming down from the Demand Frontier) and our 2) Target Price (the horizontal red line originating from the Demand Frontier).

To support that price, we’ll need to offer our customers something they like, here as Features A and B, which show up as the green Value Space at left, with the target Price as the horizontal red plane. We’ll need to figure out the Value Surface that the combinations of Features A and B command (the points for which we excluded from this view, for clarity). As seen on the left, there are Cost Surfaces for one or 500 units below the Value Surface. If we further bound our potential offering with Constraints (the vertical orange planes), we now have a region restricted on all sides. Conceptually, this expanse is not different than a like delimited region, such as your head.

Now, if you suspected that you had a deviated septum, your ear, nose, and throat doctor might order a CT scan, in which the doctor would develop section cut views of your head.

We can do the same thing in markets, using Financial CAT scans. Thus, after carefully setting up a 4D arrangement and taking cuts in both the Sections A and B directions, we can predict the 1) maximum Quantity Sold (reducing the 4D problem to one in 3D). Then we selected 2) the Price (dropping the remainder of undetermined dimensions to 2), 3) Feature A (the distance of the black plane from the origin, reducing the problem to 1 dimension), and 4) Feature B (the Vertical Profit Line, the final dimension). The per-unit profit line on the left times the number of units on the Demand Plane gives the projected profit.

In the process, we reduced a 4D problem to a single objective of maximum potential profit.

To complete the analysis, we’d examine all open price points and all viable combinations of the Features considering risk as well, searching for the best potential configuration.

Watch this video to see the analytical steps in action:

Announcing The Hypernomics YouTube Channel

It is the obvious which is so difficult to see most of the time.
Isaac Asimov, I, Robot

Here’s a question with a seemingly obvious answer:  How many stocks are part of the S&P 500?  If you guessed 500, you’d be close, as there are 504 companies listed there today.

You likely know that not all S&P companies have issued the same number of shares, nor do all share price match.  Too obvious?  Not really.

Consider what you were undoubtedly told if you ever took an economics class.  According to Paul Samuelson (Economics, 9th Ed., p. 63), “the equilibrium price, i.e., the only price that can last…must be at the intersection point of supply and demand curves.”  Samuelson would have you believe markets have but one equilibrium point.

But we know that is nonsense:  504 stocks in the S&P 500 form 504 quantity and price pairs.  While they are viable, all, in the language of Hypernomics, enjoy sustainable disequilibrium as their stock prices exceed their costs.

What’s really going on?  It turns out the value of products goes up as producers add features customers like.  At the same time, as prices go up, quantities sold fall.  To see this phenomenon, one must employ Hypernomics.

To find out how this works with as many as 8 dimensions, go to our new Hypernomics YouTube channel here:

#hypernomics #marketanalysis #prices #demand

Don’t Leave Money On The Table

“The more you learn, the more you earn” – Warren Buffett

In this true story, we hide the names to protect the players and don’t tell you the venue, either.

In 2014 (A), we ran three market Y value equations (not shown).  All showed that Project X was under-priced.  We find validation of these projections in 2021, as used X versions sell for more than their original $1M price (also not shown).  X sold amply, with 300 units in the market by 2021, and if C’s assumptions were correct, it made a profit, too (D).  Joy in Mudville!  But wait a minute.

Had X’s producers studied Y’s Demand Frontier (B), they might have noticed its negative slope of -1.24.  That means that at the limiting slope, had X’s price been raised to $1.34M, it would have made more revenue, despite the sales drop.  Also, with fewer units, recurring costs fall (C).

The overall effect in D is that selling Project X too cheaply costs Y both revenue and profit.

Hypernomics notes it’s easy to think that if a project makes a profit, it is doing well. But if we learn about all the market forces at work, often we’ll find well isn’t well enough.  Don’t leave money on the table because you didn’t study your market thoroughly.

#hypernomics #innovation #markets #marketanalysis #pricing #analytics

Hypernomics, Missing Dimensions, & Price Determination: 2nd in a Series

“Everything must be made as simple as possible. But not simpler.” ― Albert Einstein

In the last post, Paul Samuelson said equilibrium prices exist where supply meets demand.

While prices for simple products work that way, Value analyst Sheila (A) suspects markets for more complicated products behave differently.  She knows she can account for mountaintops using latitude, longitude, and altitude referencing the equator, prime meridian, and sea level, respectively (B).

With each of the 44 dots representing a unique flat screen tv’s features and price, she finds she can plot the model’s size (C) or cycles per second (D) against prices and get significant but mediocre R2s.  She works to improve her prediction.

In E, she discovers she can plot Price (Dim 3) against the tv’s refresh rate (Hz, Dim 1) and its size (Diag. “, Dim 2) as ordered triples, using an origin of (0,0,0) as a starting point.  With Hz and Diag. “ as Valued Features 1 and 2, respectively, she predicts flat-screen Value (as sustainable prices) with an R2 of 97.0% and a P-Value of 4.85E-32.  She accounts for other features as needed.

All multi-attribute markets have similar “lost dimensions.”

#markets #innovation #hypernomics #prices #dimensions #wsu

Bounding Problems

In countless games, the parameters, once set, never vary. There are only a given number of spaces on the chessboard. American football always uses an elliptical spheroid built to strict specifications. All NBA basketball rims are the same diameter, ten feet in the air.

Markets seem different. They don’t have instruction manuals. At first glance, it would seem you could do anything you want in them.

Still, they have rules. Not understanding them can sink a project.

In addition to DeLorean not understanding the value of horsepower (see the last post), they also failed to appreciate the Demand Frontier they faced in 1981. As A shows us, DeLorean thought they could exceed that limit by nearly two standard deviations, despite no one else beating it by half that much.

In this and many other markets, Product Market Demand Curves form. Always flatter than the overall Demand Frontier they build, they describe the product price limits as quantities sold increase. These curves set boundaries producers must consider before they enter any market. Ignoring them can have disastrous consequences.

How do Product Market Demand Curves compare to their Learning Curves? Look to the next post for answers.

#markets #demand #pricing #boundary #innovation #business

DeLorean CSI

In October 1982, the US government charged John DeLorean with cocaine trafficking in a deal he thought would stave off bankruptcy for his self-named company.

How did it all go wrong?

His DeLorean Motor Company sports car (A) came with several sales features. It had a rear-mounted engine, brushed stainless steel body panels, and its iconic gull-wing doors. Its original designation was the DMC-12, the “12,” reflecting its price, in thousands. But, when it came time to start taking orders, DeLorean dropped the name and raised the price.

The renamed DeLorean entered the market with 130 horsepower, priced at $25,000. As we see in B, no car with that amount of power came close to its price. The 1981 Audi 5000 Turbo, with the same horsepower, sold for $7,000 less.
Statistics reveal the sustainable prices for 1981 cars were a function of their horsepower and units sold (both P-values < 0.01).

As shown in C, the DeLorean’s predicted sustainable price was $15,500; its posted price was nearly three standard deviations too high.

To sell all 7,500 units it produced for $25K, D shows us its installed horsepower should have doubled to 262. As 1981 ended, it only sold 3,000.

Moral of the story: do market math.

#innovation #marketanalysis #valueanalysis #pricing #cars

Visualizing Value

In Multidimensional Economics, Value is a sustainable product price based on its features.  Producers set Prices.  Customers determine Value.  When they don’t match, problems arise.  Buyers pay no mind to cost when considering Value.  If you paid $1000 for a laptop, you don’t care if its cost was $1900, $900, or $90.  You just know it satisfied your Value proposition.  How do markets establish Value?

Value is whatever the market says it is.  For business jets, Fig. A shows us there is a positive correlation between speed and price.  The faster the planes go, the more buyers who can are willing to pay.  Note, though; there is high variation in A near 560 MPH, reflected in the Mean Absolute Percentage Error in D.  Fliers like to be able to take people along with them; thus, it makes sense in B that buyers pay for added capacity.  No one wants to be cramped, either, so observe in C that taller cabins fetch more money than shorter ones.  As we add features B & C, we lower errors in D.

Aircraft speed, capacity, and comfort value terms are analogous to those for computers. Laptop buyers want processor speed, short- and long-term memory, and easy to read screens.

Analysts should consider all features markets find useful.

#business #value #marketanalysis #price #innovation

Solve Profit First

Suppliers make products and see what markets will bear for them.  That’s precisely backward.

Instead, we can solve for profit potential first and discover product specifications second.

Suppose a market has products for which there are particular quantities, and prices demanded, as shown by the red dots.  We want to avoid competition, so we choose a Target Price, 1, that exploits a price gap.  Given a Demand Frontier, this sets a quantity limit, 2.

With some work (not shown), we find the market supports Features A & B with a green Value Surface (supportable prices based on those features), and that there’s an area of interest with no competition.  Linked to that region are the costs for 1 and 200 units of our new product.  If we constrain the problem (orange planes), we form an enclosure.

We then run Financial Catscans through this region.  Much like brain scans, they are virtual market section cuts.  At the optimum, we solve for the specs of Features A (3) and B (4), and the per-unit profit (5).  Per unit profit (5) times the demand limit quantity (2) yields max potential profit.

In the process, we’ve solved a 4D problem (Feature A, Feature B, Price, Quantity) from a 1D goal (profit).

#innovation #price #value #markets #profit #sales #manangement

Cannabis Laffer Curve Expanded:
The Netherlands Sparked North American Interest

In an earlier post, we examined the recreational pot tax structure.  Using US-only data, we discovered that at its frontier, a Laffer Curve formed that described the maximum amount of tax revenues possible given specific tax rates.

Here we entertain other authorities taxing legal recreational pot.  Added to the blue points forming a limit is another describing the tax rate and revenue per user for The Netherlands (NL) in 2008 (adjusted for inflation).  Through these blue points, the Laffer Curve explains 90% of their variation and is highly negative (power exponent -1.61).

Also considered now but not part of the Laffer Curve is the recent experience of British Columbia (BL 2019).  Observe it registered minuscule tax revenues.

At least 3 factors influence cannabis tax receipts: 1) Ease of legal access: BC, OR, and CA lag far behind their better-organized counterparts in making legal recreational marijuana sufficiently available.  2) Tax rate: From 15.3% (NV 2019) to 108% (WA 2014), revenues go up as tax percentages go down.  3) The proximity of lower-cost options: some would-be CA or CA tourist receipts or go to NV or black markets.

#laffercurve #market #marketanalysis #price #taxpolicy #demand #tax

Walk This Way – It Could Be More Lucrative

How do businesses’ pay to give workers a short stroll to amenities?  Using open-source data, we find companies buy easy access to nearby banks, stores, and cafes as they pay for office space and zip codes.

In A, we see LA commercial real estate prices rise with square footage and nearby household income (P-values 3.82E-16 and 0.01%, respectively), as shown by the surface.  Included in the calculation of that log-linear plane is “Walk Score,” which “measures the walkability of any address (, no affiliation with me).”

B shows the Walk Scores of 60 properties versus their prices.  Walk Score is a statistically significant (P-value 0.69%) contributor to Value (as sustainable prices).  The overall equation uses square footage, household income, and Walk Score.  It has an adjusted R^2 of 71.8%, implying there’s more work to do.

Figures C and D reveal that in Feb 2020 LA, doubling the Walk Score more than proportionally lifted the sustainable price.  Firms wishing to put up a new facility need to know this.  If the added Value of a new building exceeds its added cost, it may be worthwhile to set it up in high walkability areas.

Is NYC like LA? Look at the next post.

#price #marketanalysis #marketintelligence #realestate #target